update LAB5

    operator1 = np.array([-1, -2, -1, 0, 0, 0, 1, 2, 1])

    operator2 = np.array([-1, 0, 1, -2, 0, -2, -1, 0, 1])

    for i in range(m):

        for j in range(n):

            local = np.zeros(9)

    output_image = input_image

    output_image -= np.min(output_image)

    output_image = (output_image/np.max(output_image))*255

    output_image = output_image.astype(np.uint8)

    return output_image

# LAB 4

def convolution_3x3(input_image, operator_3x3):

    col, row = input_image.shape

    output_image = np.zeros([col, row])

    operator = np.zeros(9)

    for i in range(3):

        for j in range(3):

            operator[i*3+j] = operator_3x3[i, j]

    for i in range(col):

        for j in range(row):

            local = np.zeros(9)

            index = 0

            for i2 in [-1, 0, 1]:

                for j2 in [-1, 0, 1]:

                    if (i + i2 < 0 or i + i2 > col - 1 or j + j2 < 0 or j + j2 > row - 1):

                        local[index] = 0

                    else:

                        local[index] = input_image[i + i2, j + j2]

                    index += 1

            output_image[i, j] = np.dot(operator, local)

    output_image = output_image.astype(np.uint8)

    input_image = np.pad(input_image, 1)

    for i in range(0, col):

        for j in range(0, row):

            for i2 in range(3):

                for j2 in range(3):

                    output_image[i, j] += input_image[i+i2, j+j2] * operator_3x3[i2, j2]

    return output_image

def generating_from_spatial_filter(input_filter, P, Q):

    output_filter = np.zeros(P, Q)

    return output_filter